4 citations · 7 across the 17 of their papers we have counts for
16 papers · 1 filter
From Draft to Draft-Free: One-Step Video Object Removal via Privileged Distillation and Fast Planting
Zizhao Chen, Ping Wei, Guang Dai +2
Video object removal is a fundamental yet challenging task in video editing. Despite recent progress, existing methods typically fall into two categories. Traditional approaches ba…
Unlocking the Potential of Grounding DINO in Videos: Parameter-Efficient Adaptation for Limited-Data Spatial-Temporal Localization
Zanyi Wang, Fan Li, Dengyang Jiang +4
Spatio-temporal video grounding (STVG) aims to localize queried objects within dynamic video segments. Prevailing fully-trained approaches are notoriously data-hungry. However, gat…
SRA 2: Variational Autoencoder Self-Representation Alignment for Efficient Diffusion Training
Mengmeng Wang, Dengyang Jiang, Liuzhuozheng Li +6
Denoising-based diffusion transformers, despite their strong generation performance, suffer from inefficient training convergence. Existing methods addressing this issue, such as R…
Deforming Videos to Masks: Flow Matching for Referring Video Segmentation
Zanyi Wang, Dengyang Jiang, Liuzhuozheng Li +6
Referring Video Object Segmentation (RVOS) requires segmenting specific objects in a video guided by a natural language description. The core challenge of RVOS is to anchor abstrac…
TriCLIP-3D: A Unified Parameter-Efficient Framework for Tri-Modal 3D Visual Grounding based on CLIP
Fan Li, Zanyi Wang, Zeyi Huang +3
3D visual grounding allows an embodied agent to understand visual information in real-world 3D environments based on human instructions, which is crucial for embodied intelligence.…
Instructing Text-to-Image Diffusion Models via Classifier-Guided Semantic Optimization
Yuanyuan Chang, Yinghua Yao, Tao Qin +3
Text-to-image diffusion models have emerged as powerful tools for high-quality image generation and editing. Many existing approaches rely on text prompts as editing guidance. Howe…
No Other Representation Component Is Needed: Diffusion Transformers Can Provide Representation Guidance by Themselves
Dengyang Jiang, Mengmeng Wang, Liuzhuozheng Li +6
Recent studies have demonstrated that learning a meaningful internal representation can accelerate generative training. However, existing approaches necessitate to either introduce…
AffordanceSAM: Segment Anything Once More in Affordance Grounding
Dengyang Jiang, Zanyi Wang, Hengzhuang Li +7
Building a generalized affordance grounding model to identify actionable regions on objects is vital for real-world applications. Existing methods to train the model can be divided…
Visual Object Tracking across Diverse Data Modalities: A Review
Mengmeng Wang, Teli Ma, Shuo Xin +5
Visual Object Tracking (VOT) is an attractive and significant research area in computer vision, which aims to recognize and track specific targets in video sequences where the targ…
Low-Biased General Annotated Dataset Generation
Dengyang Jiang, Haoyu Wang, Lei Zhang +5
Pre-training backbone networks on a general annotated dataset (e.g., ImageNet) that comprises numerous manually collected images with category annotations has proven to be indispen…
SpotActor: Training-Free Layout-Controlled Consistent Image Generation
Jiahao Wang, Caixia Yan, Weizhan Zhang +6
Text-to-image diffusion models significantly enhance the efficiency of artistic creation with high-fidelity image generation. However, in typical application scenarios like comic b…
OneActor: Consistent Character Generation via Cluster-Conditioned Guidance
Jiahao Wang, Caixia Yan, Haonan Lin +5
Text-to-image diffusion models benefit artists with high-quality image generation. Yet their stochastic nature hinders artists from creating consistent images of the same subject.…
TryOn-Adapter: Efficient Fine-Grained Clothing Identity Adaptation for High-Fidelity Virtual Try-On
Jiazheng Xing, Chao Xu, Yijie Qian +5
Virtual try-on focuses on adjusting the given clothes to fit a specific person seamlessly while avoiding any distortion of the patterns and textures of the garment. However, the cl…
DreamSalon: A Staged Diffusion Framework for Preserving Identity-Context in Editable Face Generation
Haonan Lin, Mengmeng Wang, Yan Chen +6
While large-scale pre-trained text-to-image models can synthesize diverse and high-quality human-centered images, novel challenges arise with a nuanced task of "identity fine editi…
M2-CLIP: A Multimodal, Multi-task Adapting Framework for Video Action Recognition
Mengmeng Wang, Jiazheng Xing, Boyuan Jiang +6
Recently, the rise of large-scale vision-language pretrained models like CLIP, coupled with the technology of Parameter-Efficient FineTuning (PEFT), has captured substantial attrac…
MA-FSAR: Multimodal Adaptation of CLIP for Few-Shot Action Recognition
Jiazheng Xing, Chao Xu, Mengmeng Wang +5
Applying large-scale vision-language pre-trained models like CLIP to few-shot action recognition (FSAR) can significantly enhance both performance and efficiency. While several stu…